Role Overview

We are seeking a highly experienced Principal AI Architect with strong hands-on engineering expertise in modern AI systems, including LLMs, agentic workflows, and enterprise-scale integrations. This role involves working closely with clients to understand complex business problems and translating them into scalable, production-ready AI architectures. The ideal candidate combines deep technical expertise with architectural thinking to design, build, and guide the development of intelligent systems.

Key Responsibilities

• Engage with clients and stakeholders to understand business challenges and define structured problem statements.

• Translate business requirements into scalable, technically feasible AI architectures.

• Design end-to-end AI systems, including data ingestion, retrieval, reasoning, and orchestration layers. • Architect and implement Agentic RAG (Retrieval-Augmented Generation) systems.

• Design and develop multi-agent systems using patterns such as planner– executor architectures.

• Build and orchestrate AI-driven workflows for complex enterprise use cases.

• Design and implement stateful agent systems, including memory architectures (short-term and long-term memory).

• Integrate vector databases (ChromaDB, Milvus, PGVector) for semantic search and retrieval.

• Develop and integrate knowledge graphs to enhance contextual reasoning.

• Implement LLM guardrails and evaluation frameworks (e.g., RAGAS, DeepEval) to ensure reliability and performance.

• Design data ingestion pipelines for structured and unstructured enterprise data sources.

• Build modular, maintainable systems using SOLID principles and best software engineering practices.

• Develop and deploy AI services using microservices-based architectures and APIs.

• Conduct feasibility studies and architectural analysis forintegrating AI with legacy systems (e.g., SAP ecosystems).

• Create detailed technical specifications, architecture diagrams, and design documentation.

• Implement observability and tracing using tools like LangSmith for debugging and monitoring AI workflows.

• Mentorjunior engineers and provide technical leadership across AI initiatives.

Required Skills s Expertise
• Strong proficiency in Python programming.

• Deep experience with LLM systems, prompt engineering, and agentic architectures.

• Hands-on expertise in Agentic RAG systems and retrieval pipelines.

• Experience with vector databases (ChromaDB, Milvus, PGVector).

• Knowledge of LLM evaluation and guardrails frameworks (RAGAS, DeepEval).

• Experience designing multi-agent systems and AI workflows.

• Strong understanding of state management and memory systems in AI agents.

• Experience with LangChain ecosystem and observability tools (LangSmith).

• Solid understanding of microservices architecture and API-driven systems.

• Experience designing scalable AI architectures for enterprise environments.

• Familiarity with knowledge graph design and integration.

• Strong foundation in Machine Learning and Deep Learning concepts.

• Experience working with AWS and Docker.

• Strong problem-solving and analyticalthinking abilities.

• Excellent communication skills for clientinteraction and technical documentation.

Preferred Qualifications

• Experience deploying AI systems in production environments at scale.

• Exposure to enterprise-grade AI system design (security, scalability, cost considerations).

• Familiarity with distributed systems and cloud-native architectures.

• Prior experience mentoring orleading engineering teams.